@conference {12683,
	title = {Multiple view tracking of humans modelled by kinematic chains},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1009 - 1012 Vol.2 - 1009 - 1012 Vol.2},
	abstract = {We use a kinematic chain to model human body motion. We estimate the kinematic chain motion parameters using pixel displacements calculated from video sequences obtained from multiple calibrated cameras to perform tracking. We derive a linear relation between the 2D motion of pixels in terms of the 3D motion parameters of various body parts using a perspective projection model for the cameras, a rigid body motion model for the base body and the kinematic chain model for the body parts. An error analysis of the estimator is provided, leading to an iterative algorithm for calculating the motion parameters from the pixel displacements. We provide experimental results to demonstrate the accuracy of our formulation. We also compare our iterative algorithm to the noniterative algorithm and discuss its robustness in the presence of noise.},
	keywords = {3D, algorithm;, analysis;, body, calibrated, cameras;, chain, displacement;, error, estimation;, human, image, iterative, kinematic, kinematics;, methods;, model;, MOTION, motion;, multiple, parameters;, perspective, Pixel, processing;, projection, sequences;, signal, tracking;, video, view},
	doi = {10.1109/ICIP.2004.1419472},
	author = {Sundaresan, A. and Chellapa, Rama and RoyChowdhury, R.}
}
@conference {13236,
	title = {HRTF personalization using anthropometric measurements},
	booktitle = {Applications of Signal Processing to Audio and Acoustics, 2003 IEEE Workshop on.},
	year = {2003},
	month = {2003/10//},
	pages = {157 - 160},
	abstract = {Individualized head related transfer functions (HRTFs) are needed for accurate rendering of spatial audio, which is important in many applications. Since these are relatively tedious to acquire, they may not be acceptable for some applications. A number of studies have sought to perform simple customization of the HRTF. We propose and test a strategy for HRTF personalization, based on matching certain anthropometric ear parameters with the HRTF database, and the incorporation of a low-frequency "head-and-torso" model. We present preliminary tests aimed at evaluation of this customization. Results show that the approach improves both the accuracy of the localization and subjective perception of the virtual auditory scene.},
	keywords = {acoustic, anthropometric, audio, audio;, auditory, Ear, functions;, Head, head-and-torso, HRTF, individualized, localization;, measurements;, model;, models;, parameters;, perception;, personalization;, physiological, processing;, related, scattering;, scene;, signal, sound, spatial, subjective, transfer, virtual, wave},
	doi = {10.1109/ASPAA.2003.1285855},
	author = {Zotkin,Dmitry N and Hwang,J. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13243,
	title = {Human body pose estimation using silhouette shape analysis},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {263 - 270},
	abstract = {We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.},
	keywords = {3D, analysis;, body, classification;, clutter;, detection;, estimation;, extraction;, feature, function;, human, image, likelihood, multiple, object, parameter, parameters;, Pixel, pose, probability;, segmentation;, segmentations;, SHAPE, silhouette, structure;, surveillance;, views;},
	doi = {10.1109/AVSS.2003.1217930},
	author = {Mittal,A. and Liang Zhao and Davis, Larry S.}
}
